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How to analyse data after applying pandas' groupby function?

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1

I have a data set of Olympic games medal winners. I am trying to find the country with most medals. How do I go about working with the series after applying groupby function?

Here is my data frame.

 ID Name Sex Age City Sport Medal
 0 1 A Dijiang M 24.0 Barcelona Basketball Gold
 1 2 A Lamusi M 23.0 London Judo Silver
 ...

I applied the following function to my data frame called qq:

zz = qq[qq.Medal =='Gold'].groupby(['NOC', 'Medal'])
zz.Medal.value_counts()

NOC Medal Medal
ALG Gold Gold 5
ANZ Gold Gold 20
ARG Gold Gold 91
ARM Gold Gold 2

After applying the function how can I analyse this zz series?

For example how can I return the country with maximum medals?
If I groupby without 'Gold' medal constraint, how can I count the sum of medals for each country?

python dataset pandas

share|improve this question

asked Mar 31 at 2:59

a_a_aa_a_a

968

migrated from datascience.stackexchange.com Mar 31 at 16:35

This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Does each and every record have a medal? In that case remove the qq.Medal =='Gold' and 'Medal'.
  
  – Esmailian
  Mar 31 at 9:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  No. Not every country won a medal.
  
  – a_a_a
  Mar 31 at 20:24
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  what are the possible values in Medal column?
  
  – Esmailian
  Mar 31 at 20:33
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Gold Silver Bronze NaN
  
  – a_a_a
  Apr 1 at 5:05
  

  
  
  
  

add a comment | 

1

I have a data set of Olympic games medal winners. I am trying to find the country with most medals. How do I go about working with the series after applying groupby function?

Here is my data frame.

 ID Name Sex Age City Sport Medal
 0 1 A Dijiang M 24.0 Barcelona Basketball Gold
 1 2 A Lamusi M 23.0 London Judo Silver
 ...

I applied the following function to my data frame called qq:

zz = qq[qq.Medal =='Gold'].groupby(['NOC', 'Medal'])
zz.Medal.value_counts()

NOC Medal Medal
ALG Gold Gold 5
ANZ Gold Gold 20
ARG Gold Gold 91
ARM Gold Gold 2

After applying the function how can I analyse this zz series?

For example how can I return the country with maximum medals?
If I groupby without 'Gold' medal constraint, how can I count the sum of medals for each country?

python dataset pandas

share|improve this question

asked Mar 31 at 2:59

a_a_aa_a_a

968

migrated from datascience.stackexchange.com Mar 31 at 16:35

This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Does each and every record have a medal? In that case remove the qq.Medal =='Gold' and 'Medal'.
  
  – Esmailian
  Mar 31 at 9:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  No. Not every country won a medal.
  
  – a_a_a
  Mar 31 at 20:24
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  what are the possible values in Medal column?
  
  – Esmailian
  Mar 31 at 20:33
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Gold Silver Bronze NaN
  
  – a_a_a
  Apr 1 at 5:05
  

  
  
  
  

add a comment | 

I have a data set of Olympic games medal winners. I am trying to find the country with most medals. How do I go about working with the series after applying groupby function?

Here is my data frame.

 ID Name Sex Age City Sport Medal
 0 1 A Dijiang M 24.0 Barcelona Basketball Gold
 1 2 A Lamusi M 23.0 London Judo Silver
 ...

I applied the following function to my data frame called qq:

zz = qq[qq.Medal =='Gold'].groupby(['NOC', 'Medal'])
zz.Medal.value_counts()

NOC Medal Medal
ALG Gold Gold 5
ANZ Gold Gold 20
ARG Gold Gold 91
ARM Gold Gold 2

After applying the function how can I analyse this zz series?

For example how can I return the country with maximum medals?
If I groupby without 'Gold' medal constraint, how can I count the sum of medals for each country?

python dataset pandas

share|improve this question

asked Mar 31 at 2:59

a_a_aa_a_a

968

 ID Name Sex Age City Sport Medal
 0 1 A Dijiang M 24.0 Barcelona Basketball Gold
 1 2 A Lamusi M 23.0 London Judo Silver
 ...

zz = qq[qq.Medal =='Gold'].groupby(['NOC', 'Medal'])
zz.Medal.value_counts()

NOC Medal Medal
ALG Gold Gold 5
ANZ Gold Gold 20
ARG Gold Gold 91
ARM Gold Gold 2

share|improve this question

asked Mar 31 at 2:59

a_a_aa_a_a

968

share|improve this question

asked Mar 31 at 2:59

a_a_aa_a_a

968

asked Mar 31 at 2:59

a_a_aa_a_a

968

asked Mar 31 at 2:59

a_a_aa_a_a

968

1 Answer
1

active

oldest

votes

0

You need to first filter out the NaN medals, and then aggregate. Here is an example:

import pandas as pd

df = pd.DataFrame([['USA', 'Gold'],
 ['USA', 'Bronze'],
 ['USA', 'NaN'],
 ['UK', 'Silver'],
 ['UK', 'NaN']],
 columns=['NOC', 'Medal'])

valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
 .count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])

Output:

 NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object

share|improve this answer

answered Apr 1 at 13:12

EsmailianEsmailian

12112

add a comment | 

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0

You need to first filter out the NaN medals, and then aggregate. Here is an example:

import pandas as pd

df = pd.DataFrame([['USA', 'Gold'],
 ['USA', 'Bronze'],
 ['USA', 'NaN'],
 ['UK', 'Silver'],
 ['UK', 'NaN']],
 columns=['NOC', 'Medal'])

valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
 .count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])

Output:

 NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object

share|improve this answer

answered Apr 1 at 13:12

EsmailianEsmailian

12112

add a comment | 

0

You need to first filter out the NaN medals, and then aggregate. Here is an example:

import pandas as pd

df = pd.DataFrame([['USA', 'Gold'],
 ['USA', 'Bronze'],
 ['USA', 'NaN'],
 ['UK', 'Silver'],
 ['UK', 'NaN']],
 columns=['NOC', 'Medal'])

valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
 .count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])

Output:

 NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object

share|improve this answer

answered Apr 1 at 13:12

EsmailianEsmailian

12112

add a comment | 

You need to first filter out the NaN medals, and then aggregate. Here is an example:

import pandas as pd

df = pd.DataFrame([['USA', 'Gold'],
 ['USA', 'Bronze'],
 ['USA', 'NaN'],
 ['UK', 'Silver'],
 ['UK', 'NaN']],
 columns=['NOC', 'Medal'])

valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
 .count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])

Output:

 NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object

share|improve this answer

answered Apr 1 at 13:12

EsmailianEsmailian

12112

import pandas as pd

df = pd.DataFrame([['USA', 'Gold'],
 ['USA', 'Bronze'],
 ['USA', 'NaN'],
 ['UK', 'Silver'],
 ['UK', 'NaN']],
 columns=['NOC', 'Medal'])

valid_medals = df[df['Medal'] != 'NaN']
medal_count = valid_medals.groupby(['NOC'], as_index=False)
 .count().sort_values(by=['Medal'],ascending=False)
print(medal_count)
print('Top country:')
print(medal_count.iloc[0])

 NOC Medal
1 USA 2
0 UK 1
Top country:
NOC USA
Medal 2
Name: 1, dtype: object

share|improve this answer

answered Apr 1 at 13:12

EsmailianEsmailian

12112

answered Apr 1 at 13:12

EsmailianEsmailian

12112

answered Apr 1 at 13:12

EsmailianEsmailian

12112